Data Mining, Weka Decision Trees

Abstract

Nowadays, computer technologies are increasing rapidly. Thanks to the development of computer technologies, large and complex raw data sets can be transformed into useful information with different analysis techniques. Different algorithms developed thanks to computer technologies can offer different solutions to scientists and users working in different branches of science, especially engineering sciences, mathematics, medicine, industry, financial/economic fields, marketing, education, multimedia and statistics. Thanks to these solutions, it is possible to easily achieve the desired goals and objectives. Thus, by correctly managing and analyzing existing data in large and complex raw data datasets, accurate predictions can be made to be used in similar problems in the future. Data sets are analyzed and evaluated using different methods. It is also possible that the classification of data during the analysis and evaluation stages of data sets significantly affects the decision-making process regarding the work to be done. Classification of data can be done by statistical method or data mining method. Decision trees, which can be used to classify numerical and alphanumeric data, generally provide a great advantage for decision makers in terms of easy interpretation and understandability compared to other classification techniques. For these reasons, in this study, decision trees, one of the most used classification techniques in data mining, are mentioned.